The idea that translation fidelity might play a role in aging dates back at least as far as 1963, when Leslie Orgel proposed the “error catastrophe” theory of aging: in this model, mistranslation of the translational machinery creates a feedback loop that leads to further translation errors, ultimately causing loss of cell viability. From the Science of Aging Timeline:

Orgel considers two types of proteins: those involved in metabolism, and those involved in information processing. For metabolic proteins, translational error isn’t a long-term problem for the cell, since a malfunctioning protein is simply one of many. Likewise, for translational errors causing loss of function in information processing proteins: the error isn’t heritable, and a small decrease in the efficiency of gene expression is unlikely to pose a serious problem.

However, information processing proteins can be altered in another way: by mutations that decrease the fidelity with which they process or propagate genetic information. Lower-fidelity transcription and translation will result in more mutations. This is the core of Orgel’s idea: “errors which lead to a reduced specificity of an information-handling enzyme lead to an increasing error frequency. Such processes are clearly cumulative and…in the absence of an imposed selection for “accurate” protein-synthesizing units, must lead ultimately to an error catastrophe; that is, the error frequency must reach a value at which one of the processes necessary for the existence of viable cell becomes critically inefficient.”

The logic of the feedback loop is compelling, but the theory suffered for lack of experimental verification. While there is still some controversy over whether error catastrophe has received a full and fair experimental test, the consensus appears to be that while error catastrophe can take place under some systems (e.g., viral replication in the presence of drugs that reduce polymerase fidelity), this phenomenon does not play a role in mammalian aging: the measured values of the relevant parameters (basal translation error rates; the likelihood that a given error will result in further alteration to translation fidelity; protein lifetimes; etc.) appear to be such that the feedback loop doesn’t actually occur.

The error catastrophe theory is still an important waypoint in the evolution of theories of aging, and it has had tremendous influence in other areas within biogerontology. For example, similar logic has been applied to the role of autophagy in aging, where the feedback loop is called the garbage catastrophe.

And even if the feedback-loop logic doesn’t hold up to experimental scrutiny, recent findings have revealed that there may nonetheless be a relationship between protein translation fidelity and aging. Writing in PLoS ONE, Silva et al. report that in yeast, increasing the rate of translation errors might increase the activity of the longevity assurance gene SIR2:

The Yeast PNC1 Longevity Gene Is Up-Regulated by mRNA Mistranslation

Translation fidelity is critical for protein synthesis and to ensure correct cell functioning. Mutations in the protein synthesis machinery or environmental factors that increase synthesis of mistranslated proteins result in cell death and degeneration and are associated with neurodegenerative diseases, cancer and with an increasing number of mitochondrial disorders. Remarkably, mRNA mistranslation plays critical roles in the evolution of the genetic code, can be beneficial under stress conditions in yeast and in Escherichia coli and is an important source of peptides for MHC class I complex in dendritic cells. Despite this, its biology has been overlooked over the years due to technical difficulties in its detection and quantification. In order to shed new light on the biological relevance of mistranslation we have generated codon misreading in Saccharomyces cerevisiae using drugs and tRNA engineering methodologies. Surprisingly, such mistranslation up-regulated the longevity gene PNC1. Similar results were also obtained in cells grown in the presence of amino acid analogues that promote protein misfolding. The overall data showed that PNC1 is a biomarker of mRNA mistranslation and protein misfolding and that PNC1-GFP fusions can be used to monitor these two important biological phenomena in vivo in an easy manner, thus opening new avenues to understand their biological relevance.

PNC1 is a longevity gene because its biochemical activity feeds into the sirtuin pathway: Pnc1p synthesizes nicotinic acid from nicotinamide, which is an inhibitor of Sir2p, one of the canonical longevity factors in S. cerevisiae. Overexpression of PNC1 increases lifespan, presumably by increasing the activity of Sir2p. (The authors show that Sir2p silencing activity is elevated under conditions that cause mistranslation, and that this is inhibited by exogenous nicotinamide. Missing, as far as I can tell, is the same experiment in ∆pnc1 cells, which according to the authors’ model would not induce silencing during mistranslation.)

Is this simply an example of a general stressor activating a general stress response, whose constitutive activation in turn makes cells more stress-resistant and therefore longer-lived? For example, one could imagine a translation fidelity problem resulting in synthesis of lots of poorly folded proteins, leading to activation of the heat shock response and expression of chaperones (indeed, in the worm, heat shock transcription factor HSF-1 is required for life extension by daf-2 mutations). This doesn’t appear to be that. Instead, loss of protein fidelity causes upregulation of a major longevity assurance pathway, which acts primarily at the level of transcriptional silencing.

A couple of questions:

  • What is the relevant molecular correlate of translation infidelity? Unfolded proteins would be the most likely culprit (prediction: whether or not it’s involved in the lifespan extension, there should be some heat shock response under these conditions), but one can imagine more elaborate scenarios: Suppose an inhibitor of PNC1 translation is encoded by an mRNA that is particularly likely to be mistranslated under normal conditions (e.g., because of weird codon usage, secondary structure, or some other quirk) and is now translated so poorly that it loses its inhibitory activity altogether (or acquires a new activity).
  • How is the translational upregulation of PNC1 mediated? This is particularly curious given that, by assumption, a cell with a high rate of translation infidelity is having difficulty with translation. Teleologically, there’s no reason not to regulate gene expression at this level — if the gene were upregulated transcriptionally, the mRNA would still have to be translated — but it still strikes me as odd. If this is a bona fide evolved response to translation problems, wouldn’t it be better to pre-synthesize PNC1 and then activate it post-translationally (e.g. by proteolysis)?
  • Is SIR2 involved in translation fidelity? Looking at this story as a straightforward stress response, one would expect some action of SIR2 to help mitigate the stress that started the whole process. So I’d be curious to know whether SIR2 mutants have lower translation fidelity, and if so, how it is that SIR2 is involved in improving the accuracy of translation?

ResearchBlogging.orgSilva, R., Duarte, I., Paredes, J., Lima-Costa, T., Perrot, M., Boucherie, H., Goodfellow, B., Gomes, A., Mateus, D., Moura, G., & Santos, M. (2009). The Yeast PNC1 Longevity Gene Is Up-Regulated by mRNA Mistranslation PLoS ONE, 4 (4) DOI: 10.1371/journal.pone.0005212

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Aging is what geneticists like to call a “complex trait” — simply put, a trait that is controlled by a large number of genes and the interactions between them. Complex traits differ from simple traits in the following way: When one is studying a simple trait, one simply identifies a mutant in the relevant trait and, after an ingenious combination of clever crosses and muscular cloning steps, finds the defective gene — thus gaining a great deal of explanatory power about the trait in question.

When one is studying a complex trait, however, approaches like a mutant screen fall short. They don’t fall totally flat — one of the great innovations of the last decade or so is the realization that we can learn quite a bit by studying aging at the single-gene level — but they can’t get us all the way home. Suppose you do a screen and find fifty mutants that all lengthen lifespan by forty percent (not far from the situation in worm) — or, speaking more generally about complex traits, you find fifty loci in the human genome that are associated with a higher risk of schizophrenia. What then? What have you really learned about how the system works?

In order to really get a handle on complex traits like aging, we need new tools — not only to discover genes involved in our favorite traits, but also the interactions between the gene’s products and the environment. Indeed, we need a whole new toolbox, something that would be barely recognizable to the geneticists of fifty years ago.

According to the principle that one should simultaneously wage as few battles at possible, the development of the new toolbox for analysis of complex traits won’t happen in the most complex organisms. Instead, we will look to the simplest and most malleable models in which to test our new techniques.

That’s just what Lorenz et al. have accomplished in a recent study. Their simple model was yeast, a reliable workhorse in aging research since the dawn of modern biogerontology. Their toolbox is called “network inference” — perturbing the expression of single genes within a network, measuring the resulting changes throughout the transcriptome, and using this data to learn about the connectivity of the network.

A network biology approach to aging in yeast

In this study, a reverse-engineering strategy was used to infer and analyze the structure and function of an aging and glucose repressed gene regulatory network in the budding yeast Saccharomyces cerevisiae. The method uses transcriptional perturbations to model the functional interactions between genes as a system of first-order ordinary differential equations. The resulting network model correctly identified the known interactions of key regulators in a 10-gene network from the Snf1 signaling pathway, which is required for expression of glucose-repressed genes upon calorie restriction. The majority of interactions predicted by the network model were confirmed using promoter-reporter gene fusions in gene-deletion mutants and chromatin immunoprecipitation experiments, revealing a more complex network architecture than previously appreciated. The reverse-engineered network model also predicted an unexpected role for transcriptional regulation of the SNF1 gene by hexose kinase enzyme/transcriptional repressor Hxk2, Mediator subunit Med8, and transcriptional repressor Mig1. These interactions were validated experimentally and used to design new experiments demonstrating Snf1 and its transcriptional regulators Hxk2 and Mig1 as modulators of chronological lifespan. This work demonstrates the value of using network inference methods to identify and characterize the regulators of complex phenotypes, such as aging.

It’s sort of like pulling on one strand of spider silk and watching how other strands move, in order to build a model about the connectedness of the entire web. Like much modern systems biology, the idea is to study the parts and learn about the whole.

In their first foray, they re-discovered the known interactions in a calorie restriction-regulated gene network in yeast. Before you yawn: rediscovery of prior knowledge is an important validation for a new technique; what’s additionally impressive is that this system, in just a few weeks of experimentation and analysis, was able to recapitulate the results of (literally) years of prior work. Beyond that, the authors were able to detect evidence of novel (i.e., heretofore unknown) interactions between network components.

The critic might argue that this is even duller than the validation attained by re-discovering prior knowledge — but the critic would be wrong. Whether we’re talking about schizophrenia, heart disease or aging, we ultimately want to understand complex traits well enough to intervene in them without doing more harm than good. Approaches like network inference, which reveal the fine detail of biological systems, make it possible to observe the relationships between genes we might target with drugs — as well as predict second-order effects and undesired consequences of specific types of intervention — bringing us that much closer to our goal.

TOR (target of rapamycin) integrates nutrient and energy signals in eukaryotic cells to regulate growth and cell size, and has been linked to aging in various model systems, including yeast.

Yeast is an important model system for biogerontology in general and for the study of telomere biology in particular: yeast cells grow quickly, and its genome can be manipulated with relative ease, alllowing the study of knock in/out strains. The yeast protein Cdc13p (homologous to the human protein Pot1) binds single-stranded telomeric repeats, maintaining genomic integrity and suppressing the activation of Mec1p (the yeast homologue of human ATR, which is involved in the DNA-damage response pathway). Qi et al. have previously demonstrated that inactivation of Cd13p using a temperature-sensitive mutant results in deprotection of telomeres, activation of Mec1p and either senescence or apoptosis (depending on the cellular context). These same authors have continued their studies and now report that TOR plays a pivotal role in the apoptosis that results from deprotected yeast telomeres:

TOR Regulates Cell Death Induced by Telomere Dysfunction in Budding Yeast

Telomere dysfunction is known to induce growth arrest (senescence) and cell death. However, the regulation of the senescence-death process is poorly understood. Here using a yeast dysfunctional telomere model cdc13-1, which carries a temperature sensitive-mutant telomere binding protein Cdc13p, we demonstrate that inhibition of TOR (Target of Rapamycin), a central regulator of nutrient pathways for cell growth, prevents cell death, but not growth arrest, induced by inactivation of Cdc13-1p. This function of TOR is novel and separable from its G1 inhibition function, and not associated with alterations in the telomere length, the amount of G-tails, and the telomere position effect (TPE) in cdc13-1 cells. Furthermore, antioxidants were also shown to prevent cell death initiated by inactivation of cdc13-1. Moreover, inhibition of TOR was also shown to prevent cell death induced by inactivation of telomerase in an est1 mutant. Interestingly, rapamycin did not prevent cell death induced by DNA damaging agents such as etoposide and UV. In the aggregate, our results suggest that the TOR signaling pathway is specifically involved in the regulation of cell death initiated by telomere dysfunction.

The authors began by inactivating Cdc13p to leave the single stranded telomeric overhang permanently unprotected. This resulted in growth arrest and cell death characterized by increased ROS production, increased phosphatidylserine flipping, and caspase activation. However, treatment with rapamycin (which inhibits TOR, and which has been much discussed lately as a possible anti-aging therapeutic) significantly attenuated these effects; cell survival was increased 100-fold and the aforementioned markers of cell death were effectively inhibited.

As Cdc13p binds to telomeres in S-phase, and TOR inhibition delays the G1/S transition, the authors reasoned that the observed protective effects of rapamycin may simply reflect its effect on the cell cycle — i.e., cells do not reach S-phase, and so do not require Cdc13p. As rapamycin did not result in G1 accumulation, however, the authors concluded that the protective effect of rapamycin is unrelated to the delay in the cell cycle.

They next asked whether rapamycin affected the state of the telomeres, by examining the number of telomeric G-tails, telomere length, and the telomere-position effect (TPE; the phenomenon whereby telomere status influences gene expression in sub-telomeric regions). All three measures remained unchanged upon treatment with rapamycin, suggesting that the effect is mediated downstream of telomeric status.

Cell death can also be triggered by mechanisms that are independent of telomere dysfunction. Exposure to either UV or etoposide (a topoisomerase-II poison) induced an apoptotic response that is not rescued by rapamycin treatment, supporting the authors’ hypothesis that the drug specifically prevents telomere-initiated cell death. Inactivation of Cdc13p resulted in elevated ROS, which is abolished by rapamycin (and also by the antioxidants vitamin C and NAC), raising the possibility that rapamycin prevents telomere-initiated cell death via an antioxidant mechanism was raised.

Inhibition of TOR is known to up-regulate Sod2p, but deletion of Sod2p did not affect the protective effect of rapamycin. However, rapamycin treatment was shown to reduce mitochondiral ROS production and increase mitochondiral mass, pointing to regulation of mitochondrial function as the mechanism through which rapamycin prevents cell death. The implied model is that treatment with rapamycin improves mitochondrial bioenergetics, thus reducing the generation of ROS, which are known to impact of telomere dynamics. To provide additional support for this model, the final experiment involved inactivation of Est1p (an essential subunit of telomerase). As with Cdc13p inactivation, rapamycin treatment suppressed the elevated PS flipping and ROS production.

Taken together, the findings support the hypothesis that “the TOR signaling pathway is specifically involved in the regulation of cell death initiated by telomere dysfunction”.

I have often wondered just how telomere biology in yeast relates to telomere dynamics in higher organisms. In particular, S. cerevisiae (used in the present study) is quite different from other organisms in terms of telomere sequences and telomerase activity. A minor point perhaps, but important to note.

Yeast has taught us a great deal about the mechanisms of aging. But what about using yeast to fight the aging process itself?

A group of young scientists is trying to genetically engineer brewer’s yeast to make resveratrol, an antioxidant compound that activates sirtuins and may or may not extend mammalian lifespan (link):

A team of researchers at Rice University in Houston is working to create a beer that could fight cancer and heart disease. Taylor Stevenson, a member of the six-student research team and a junior at Rice, said the team is using genetic engineering to create a beer that includes resveratrol, the disease-fighting chemical that’s been found in red wine.

This isn’t the usual sort of thing we cover at Ouroboros. It’s a pre-pre-publication story from a decidedly non-scholarly source (ComputerWorld); and the article itself was written for such a general audience that it’s impossible to tell exactly what’s being done. Furthermore, the piece makes some bizarre claims (emphasis mine):

The students, using their own Dell, Lenovo ThinkPad and Gateway laptops, are now in the process of developing a genetically modified strain of yeast that will ferment beer and produce resveratrol at the same time. Stevenson said that as the research advances, the team will need to use one of Rice University’s computer grids to run compute-heavy genetic models.”

Really? What possible ramification of expressing a few synthases in yeast could require so much power that the researchers need to turn to grid computing? Ah well, these are the foibles of the lay press. And, possibly, the foibles of telling reporters from tech magazines the sort of thing they want to hear so that they cover your honors project.

In any case, you heard it here first. Watch your supermarket shelves for MGD: Miller Genetically-engineered Draft.

The budding yeast Saccharomyces cerevisiae has been a valuable model system in biogerontology, dating back to the very earliest years of the modern synthesis of molecular genetics with the study of lifespan regulation. From yeast we first learned about the sirtuins, and it continues to teach us much about the mechanisms of lifespan extension by calorie restriction.

Aging in yeast can be studied in one of two ways: by focusing on the replicative lifespan (RLS), which is the number of times that a mother cell can bud to form a daughter; or on the chronological lifespan (CLS), which has to do with how long a cell can persist and maintain viability in a nondividing state. While there is some overlap between the genes regulating CLS and RLS, they are generally discussed as though they are distinct phenomena.

An under-appreciated feature of yeast aging is that, at the end of CLS or RLS, a yeast cell can die either by necrosis or by programmed cell death — i.e., apoptosis or something very much like it. That comes as a surprise to those of us who grew up thinking of apoptosis as a kind of “noble sacrifice” made by a damaged cell in the context of a tissue or organ: damage leads to cancer, but not if it leads to cell death first; hence, there’s a survival benefit to the organism if individual cells “voluntarily” die in response to certain types of stress. But with no body to protect, why would a single-celled organism undergo apoptosis?

The mechanisms and evolutionary ramifications of yeast apoptosis are the subject of a review by Rockenfeller and Madeo. For those of you who have followed this story for a while, Frank Madeo was the first author of the paper that identified caspase-like enzymes operating in yeast apoptosis; that manuscript was a worldwide journal-club favorite throughout the yeast and apoptosis fields back in the early years of the 21st century.

Apoptotic death of ageing yeast

Yeast has been a valuable model to study replicative and chronological ageing processes. Replicative ageing is defined by the number of daughter cells a mother can give birth to and hence reflects the ageing situation in proliferating cells, whereas chronological ageing is widely accepted as a model for postmitotic tissue ageing. Since both ageing forms end in yeast programmed death (necrotic and apoptotic), and abrogation of cell death by deletion of the apoptotic machinery or diminishment of oxidative radicals leads to longevity, apoptosis and ageing seem closely connected. This review focuses on ageing as a physiological way to induce yeast apoptosis, which unexpectedly defines apoptosis as a pro- and not an anti-ageing mechanism.

I take issue with the last sentence in the abstract, at least as it applies to the broader field of biogerontology. Very few of us in the mammalian aging field think of apoptosis as an “anti-aging” mechanism; rather, we see it as an tumor suppressor mechanism that has negative consequences on regenerative capacity. In other words, apoptosis in adult metazoans is an anti-cancer but pro-aging phenomenon.

Calorie restriction (CR) extends lifespan in most organisms studied, including some of our more distant relatives — e.g. the baker’s brewer’s yeast Saccharomyces cerevisiae. The genetics underlying CR-mediated life extension are currently being worked out (for details, see our earlier piece, Biogerontology rising); despite lingering controversy, the story is starting to converge. Specifically, it’s becoming clear that TOR, Sch9 kinase and regulation of ribosome synthesis play an important role — and, in contrast to earlier models, it’s seeming less and less likely that sirtuins are involved.

A new twist in the plot comes from a comparative study of two budding yeasts, S. cerevisiae and its close relative Kluyveromyces lactis. Brewer’s yeast prefers to ferment (grow anaerobically) in glucose-rich environments (like an extract of malted barley), but when carbon is limiting, it starts to grow aerobically. According to Oliveira et al. this increase in respiratory capacity is essential to the lifespan extension mediated by CR in yeast:

Increased aerobic metabolism is essential for the beneficial effects of caloric restriction on yeast life span

Calorie restriction is a dietary regimen capable of extending life span in a variety of multicellular organisms. A yeast model of calorie restriction has been developed in which limiting the concentration of glucose in the growth media of Saccharomyces cerevisiae leads to enhanced replicative and chronological longevity. Since S. cerevisiae are Crabtree-positive cells that present repression of aerobic catabolism when grown in high glucose concentrations, we investigated if this phenomenon participates in life span regulation in yeast. S. cerevisiae only exhibited an increase in chronological life span when incubated in limited concentrations of glucose. Limitation of galactose, raffinose or glycerol plus ethanol as substrates did not enhance life span. Furthermore, in Kluyveromyces lactis, a Crabtree-negative yeast, glucose limitation did not promote an enhancement of respiratory capacity nor a decrease in reactive oxygen species formation, as is characteristic of conditions of caloric restriction in S. cerevisiae. In addition, K. lactis did not present an increase in longevity when incubated in lower glucose concentrations. Altogether, our results indicate that release from repression of aerobic catabolism is essential for the beneficial effects of glucose limitation in the yeast calorie restriction model. Potential parallels between these changes in yeast and hormonal regulation of respiratory rates in animals are discussed.

For those of you whose yeast metabolic biochemistry is a little bit rusty: The alternate carbon sources (carbohydrates other than glucose) are ones that S. cerevisiae must metabolize aerobically (to a greater or lesser extent: they can grow anaerobically, though poorly, on non-glucose sugars, but not at all on glycerol, which absolutely requires respiration).

To summarize: Only in S. cerevisiae and only in the context of growth on glucose metabolism (which happens anaerobically at high concentrations but aerobically at low concentrations) does CR results in lifespan extension. When limitation of a carbon source does not result in a net increase in respiration — in S. cerevisiae growing on alternate sugars, or in K. lactis, which prefers to grow aerobically even under glucose-rich conditions — CR does not extend longevity.

The title is too strongly worded for my taste. The data are ultimately correlative, and I would liketo see more genetic manipulation that tests the hypothesis: for example, using S. cerevisiae mutants that don’t undergo the shift to aerobic metabolism in response to limiting glucose, or “high respiratory” strains that respire constitutively or at least undergo the metabolic shift earlier in the glucose-limitation curve. (My K. lactis genetics is non-existent, so I don’t know whether the converse mutants — i.e., reluctant respirers or “ready fermenters” — exist in that species, but if they do, it would be nice to see whether they exhibit CR-mediated life extension.)

But given the huge contributions that yeast has made to biogerontology in general, and to CR in particular, it will be interesting to see whether CR in metazoans is also accompanied by an increase in aerobic metabolism. If so, is it required for the benefits of CR, and more importantly, what are the molecular mechanisms underlying the metabolic shift?

Our understanding of aging in animals owes a great debt to a large body of careful work in a single-celled organism, the brewer’s yeast Saccharomyces cerevisiae. Indeed, as I’ve argued before, yeast is one of the two organisms with the strongest credible claim to have started modern biogerontology. An unusually large crop of yeast aging papers have appeared over the last few months, and I thought it would be appropriate to spend a few paragraphs describing them — in honor of this humble organism that rises our bread, ferments our beer, and has done so much to open our eyes to the fundamental mechanisms of aging.

For those unfamiliar with the yeast field or simply wishing a clearly written and nearly comprehensive summary, Steinkraus et al. provide the historical perspective. The piece thoroughly reviews the development of yeast as a model system in aging, as well as the arguments in favor of a connection between results in yeast and well-established (but sometimes hard-to-test) hypotheses in animals.

Based on the influence that yeast has already had on biogerontology as a whole, it seems fair to claim that it will continue to reveal fundamentals of aging that are conserved across evolution. Now, however, there is quantitative evidence to back up that claim: Smith et al. have used bioinformatic and genomic approaches to study the conservation between known longevity genes in yeast and worm, and they show that yeast mutants in worm longevity genes are significantly more likely to be long-lived than randomly chosen mutants — suggesting that

genes that modulate aging have been conserved not only in sequence, but also in function, over a billion years of evolution.

Given this functional conservation, it is reasonable to use yeast to help answer questions about aging in general, so long as these questions are cell-biological in scope.

For instance: NAD+/NADH ratios are thought to be an important metric of the cellular energy balance, and appear to have effects both within the mitochondria and the cytosol. The mitochondrial inner membrane, however, is impermeable to both NAD+ and NADH. How, then, is information about energy balance communicated between the two cellular compartments? Easlon et al. report that two components of the malate-aspartate NADH shuttle (which transports metabolites across the mitochondrial membrane, resulting in equilibration of the cytosolic and mitochondrial NAD+/NADH pools) are involved in controlling longevity. The two proteins, Mdh1 and Aat1, are required for longevity enhancement by calorie restriction (CR), and overexpression of both proteins can increase lifespan independent of caloric conditions (but in a Sir2-dependent manner, about which see more below).

Another outstanding question involves how cellular energy balance is coordinated with the rates of catabolic and anabolic processes, and how this coordination impinges on regulation of longevity. We know that in yeast, the effects of CR are mediated by pathways involving the nutrient sensor TOR and the kinase Sch9. (Brief aside: longevity-enhancing mutations of Sch9 can also suppress genomic instability; new results from Qin et al. show that genomic instability is also associated with lifespan variation in yeast). Sch9 regulates, among other things, ribosome biogenesis; both CR and Sch9 mutation cause ribosome synthesis to decrease — but are the ribosome and longevity phenotypes related? Very likely yes: Steffen et al. report that multiple means of downregulating ribosome synthesis all extend lifespan, implying that reducing production of ribosomes is essential in order to reap the benefits of CR.

As the tools of biology have adapted, so has the yeast field (sometimes leading the charge, as in the case of the earliest microarray-based expression profiling experiments). Murakami et al. have developed a high-throughput method for measuring yeast lifespan. In this first report, the authors primarily demonstrate the use of their method on known mutants, arguing that their results are similar but with lower variance. (Brief aside: they also demonstrate that CR-induced lifespan extension does not require SIR2 or any other yeast sirtuin, adding fuel to the controversy about whether sirtuins play any role in CR in yeast; for more, see here and here.) The increased precision of their technique will allow detection of subtler aging-related phenotypes than were previously detectable, very likely allowing us to add to the list of genes known to regulate lifespan. The high-throughput aspects of the method, of course, open the door to testing small-molecule drugs that could delay aging in yeast — historically a fruitful approach though not without its potential pitfalls.

If you’ve made it this far, feel free to toast S. cerevisiae, perhaps with a beer.

(Before I depart, I just want to mention — since it’s not necessarily clear from the first authors’ names — that four of the papers mentioned above, as well as many of the papers described in earlier Ouroboros posts linked above, are the result of the combined work of the Kaeberlein and Kennedy labs at U-Wash Seattle. Both of them worked together in the Guarente lab back in the day, and they’ve been in the yeast aging field from its very beginning. Clearly, their combined work is continuing to advance the field.)